arXiv：cs.LG· Yipan Wei, Zhaokun Yan, Ziming Hong, Jiaqi Wu, Lixu Wang·· 3 小时前AI 评分36
联邦世界模型学习中的轨迹异质性研究：基于 MIMIC-IV 的基准测试
Understanding Trajectory Heterogeneity in Federated World Model Learning
AI 导读
一项研究对联邦世界模型学习中的跨时间轨迹异质性进行了基准测试，基于八个 MIMIC-IV 疾病队列的小时级动作条件临床预测，涵盖 4087 万条转移成员关系。
正文
Abstract:World models learn state evolution from trajectories, making access to temporal context a central training requirement. Federated learning can use distributed records, while ownership boundaries within a trajectory restrict the examples each client can construct. Our study benchmarks this cross-time setting through hourly action-conditioned clinical prediction on eight MIMIC-IV disease cohorts, comprising 40.87 million transition memberships. We specify severity-based client ownership, patient-separated construction, local history and future-window rules, and paired rollout evaluation from one to 32 hours. A matrix of ten federated algorithms covers 32 disease--partition configurations under five rounds of ten-percent participation. Three findings emerge from existing results and training logs. First, client ownership and participation jointly restrict long-window coverage: only 7.55\%--21.36\% of pooled-available 32-step windows have a locally complete anchor visited during training, averaged across diseases. Second, finer severity partitions accompany higher FedAvg error in 15 of 16 paired comparisons, while algorithm gains are small and horizon-dependent: FedProx reduces mean error by 0.56\%, with no consistent improvement at 32 steps. Third, algorithm labels conceal distinct update behavior, including inactive extrapolation and orders-of-magnitude differences in update scale. Cached-update performance also varies strongly across trajectory partitions under the same benchmark protocol. These results establish temporal access, participation coverage, optimization behavior, and horizon-resolved prediction as complementary dimensions for evaluating federated clinical world models.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.02957 [cs.LG] |
| (or arXiv:2610.02957v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02957 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhaokun Yan [view email]
[v1]
Fri, 2 Oct 2026 07:55:25 UTC (6,340 KB)
来源：arXiv：cs.LG · arxiv.org